Data Engineer - Data Foundations for AI (all genders)

About the role

**Role Summary** Serrala is building a unified Data Platform to enable cross‑domain outcomes across Accounts Receivable, Accounts Payable, and Payments & Cash. The Data Engineer will be a core contributor to this platform, responsible for designing, building, and operating data pipelines and data models that land, normalize, and curate data from SAP‑embedded and SaaS products into a layered Bronze / Silver / Gold architecture. The role focuses on reliable execution, data quality, and scalability within a hybrid reference architecture (cloud + customer‑managed realities), operating under strict governance and compliance requirements (GDPR, SOC2, ISO). **What You'll Do** 1) Build & Operate Data Pipelines - Implement robust data ingestion pipelines to land data into the Bronze layer with traceability, metadata, and data contracts. - Develop Silver-layer transformations to cleanse, normalize, and consolidate data across heterogeneous product semantics. - Build Gold-layer data products that are curated, well-modeled, and ready for consumption by AI and analytics use cases. - Ensure pipelines are reliable, observable, and designed for incremental evolution. 2) Implement the Standardized Data Stack - Build data pipelines and transformations using Serrala's standardized primary data stack (e.g., Azure, Databricks or Snowflake, depending on final choice). - Apply platform standards, templates, and "golden path" patterns defined by the Data Platform Architect. - Optimize pipelines for performance, scalability, and cost-awareness. 3) SAP & Product Data Integration - Implement data ingestion from SAP‑embedded products (SAP S/4HANA based solutions) using approved integration patterns. - Work with SAP Datasphere and/or SAP Business Data Cloud for analytics, data sharing, or integration scenarios where applicable. - Integrate data from cloud-native SaaS products via APIs, CDC/streaming, and file-based mechanisms. - Ensure SAP clean‑core principles are respected by using non-invasive data access patterns. 4) Data Quality, Validation & Governance - Implement data quality checks, validation rules, and anomaly detection at each layer of the platform. - Apply governance standards related to access control, encryption, retention, and auditability. - Ensure datasets meet compliance expectations for GDPR, SOC2, and ISO by design, not as an afterthought. 5) Collaboration with Product & Platform Teams Collaborate closely with Product Managers and Engineers across SAP‑embedded and SaaS products to: - Define data contracts and schemas - Align on business semantics - Ensure new product features are "data‑platform ready" - Work closely with AI platform and analytics teams to ensure data is fit for downstream consumption (e.g., consistent semantics, reliable freshness). 6) Operate the Platform in Production - Monitor pipelines, troubleshoot failures, and continuously improve reliability and performance. - Contribute to documentation, runbooks, and operational best practices. - Participate in reviews and improvements of platform standards and patterns. **Required Experience & Skills** - 4+ years of hands‑on experience as a Data Engineer in modern data platforms. - Proven experience building and operating ETL/ELT pipelines and layered data architectures. - Strong practical experience with at least one modern data stack (e.g., Azure Data Factory, Databricks, Snowflake, or equivalent). - Hands‑on experience with SAP‑centric data landscapes, including SAP S/4HANA or ECC. - Experience integrating data from SAP‑embedded systems and cloud-native SaaS products. - Familiarity with streaming/CDC concepts (e.g., Kafka) and API-based ingestion. - Solid understanding of data quality, validation, and data modeling best practices. - Awareness of governance and compliance requirements (GDPR, SOC2, ISO) in enterprise environments. - English language skills (C1/C2). **Nice-to-Have** - Practical knowledge of SAP Datasphere and/or SAP Business Data Cloud for data integration or analytics scenarios. - Experience working in hybrid environments with customer-managed constraints. - Exposure to analytics or AI/ML consumption patterns (feature-ready datasets, telemetry data). - Experience with observability, monitoring, and cost optimization for data pipelines. **Working Style / Mindset** - You enjoy turning architectural intent into reliable, production-grade data pipelines. - You care deeply about data quality, correctness, and trust. - You collaborate pragmatically with product and platform teams to ship value incrementally. - You treat governance, security, and compliance as part of engineering craftsmanship, not paperwork. **Why you'll love it here** Step into a dynamic, agile workplace where continuous learning is championed by leadership, and innovation in finance automation is fuelled by cutting-edge tech, AI integration, and strategic SAP transformation. We partner with the best to stay ahead - so

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